{"id":"W2996538552","doi":"","title":"Blind Deconvolution of Seismograms Regularized via Minimum Support","year":2010,"lang":"en","type":"article","venue":"AGU Fall Meeting Abstracts","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Seismogram; Deconvolution; Convolution (computer science); Source function; Algorithm; Blind deconvolution; Blind signal separation; Gaussian; Mathematics; Computer science; Geology; Physics; Seismology; Channel (broadcasting); Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009839471,0.0006935198,0.0006616621,0.0006784946,0.0001846071,0.0004800261,0.0007114324,0.0009758408,0.0010284],"category_scores_gemma":[0.00377216,0.0003577116,0.0006283122,0.0006368874,0.0006593513,0.001040169,0.0009347359,0.0009061866,0.0004605445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000333905,"about_ca_system_score_gemma":0.0005867123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001399992,"about_ca_topic_score_gemma":0.001278068,"domain_scores_codex":[0.9996129,0.0001446104,0.00002515576,0.00005668267,0.0001249603,0.00003577254],"domain_scores_gemma":[0.9987497,0.0007690465,0.000163681,0.0001385099,0.0001455243,0.00003347192],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008043444,0.0001291794,0.0007534286,0.000262551,0.0001340161,0.0002122484,0.0001888928,0.6951775,0.07695046,0.03120059,0.002318265,0.1918685],"study_design_scores_gemma":[0.00001497905,0.00001846874,0.000178141,0.00000552495,0.000004037155,0.0000253034,0.000005395103,0.9882604,0.004986481,0.006137078,0.0003547197,0.000009463355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01981155,0.0001050249,0.9792093,0.00009485627,0.00001526172,0.00001324374,0.00005938909,0.0002871788,0.0004041498],"genre_scores_gemma":[0.3322682,0.0004512711,0.6625408,0.00009912969,0.00008152809,0.0001262643,0.0006121516,0.0001760216,0.003644653],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001399992,"threshold_uncertainty_score":0.005203724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01608242731724071,"score_gpt":0.26346235726214,"score_spread":0.2473799299448993,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}